A Decision-Making Approach Based on Time Series to Configure a Responsive and Sustainable Supply Chain Considering Different Transportation Modes under Uncertainty

The importance of logistics activities in the nowadays modern and competitive marketplace has attracted the attention of researchers toward the supply chain management (SCM) problem. Also, by increasing environmental and social concerns, the concept of sustainability has been drastically highlighted in today’s logistics networks. Therefore, the current study focuses on designing a closed-loop supply chain based on the sustainability and responsiveness dimensions by considering different transportation modes using a data-driven model. In this research, at the outset, a mathematical model is proposed to optimize the sustainability dimensions by considering the responsiveness metric. Then, since the dynamic nature of the business environment leads to the high level of uncertainty, we combine the SARIMA (Seasonal Autoregressive Integrated Moving Average) and the Possibilistic Robust Stochastic (PRS) methods to develop a data-driven framework for tackling the mixed uncertainty. Afterward, by considering the medical equipment industry as a real-world application, a heuristic-based meta-goal programming (MGP) method is developed to solve the proposed model. The achieved results show the appropriate performance of the proposed data-driven model. Afterward, a series of sensitivity analyses are performed to evaluate how key model parameters influence the research problem, leading to the generation of relevant managerial insights.